A Case of Congenital Word-Blindness.

EEG is one of the most useful techniques used to represent behaviours of the brain and helps explore valuable insights through the measurement of brain electrical activity. Hence, plays a vital role in detecting neurological conditions. In this paper, we identify some unique EEG patterns pertaining to dyslexia, which is a learning disability with a neurological origin. Although EEG signals hold important insights of brain behaviours, uncovering these insights are not always straightforward due to its complexity. We tackle this using machine learning and uncover unique EEG signals generated in adults with dyslexia during writing and typing as well as optimal EEG electrodes and brain regions for classification. This study revealed that the greater level of difficulties seen in individuals with dyslexia during writing and typing compared to normal controls are reflected in the brainwave signal patterns.

/pdf/eeg-signal-analysis-of-writing-and-typing-between-adults-44z7nsk1it.pdf

EEG Signal Analysis of Writing and Typing between Adults with Dyslexia and Normal Controls

Dyslexia, Dysgraphia and Dyscalculia are significant learning disabilities that affect around 10% of children in the world. Despite the advancement of technology literacy in the community, limited attention has been given for screening and intervention of these disabilities using mobile applications in Sri Lanka. In this research, one of the first deep learning and machine learning based mobile applications, named “Pubudu” was developed for screening and intervention of dyslexia, dysgraphia and dyscalculia supporting local languages. In “Pubudu” we have followed up clinical screening and diagnostic procedures recommended by health professionals for screening and intervention. The screening of dyslexia, letter dysgraphia and numeric dysgraphia was carried out using deep neural network and the screening for dyscalculia was carried out using machine learning techniques. Intervention techniques are implemented using gamified environments. System testing was carried out using 50 differently abled children and 50 typical children. With the initial dataset 88%, 58%, 99% screening accuracies are achieved in neural networks for letter dysgraphia, dyslexia and numeric dysgraphia screening while dysgraphia, whereas 90% accuracy was achieved for dyscalculia. Handwritten letters and numbers were fed as inputs to CNN model in letter dysgraphia and numeric dysgraphia while embedded audio clips of letter pronunciation were fed in to voice recognition CNN model in dyslexia. “Pubudu” shows significant potential for screening and intervention of dyslexia, dysgraphia and dyscalculia in local languages motivating children and interactively making them able and would be an enabling app for most of the underprivileged children in Sri Lanka.

Pubudu: Deep Learning Based Screening And Intervention of Dyslexia, Dysgraphia And Dyscalculia

This paper aims to diagnose children with specific learning disabilities and provide treatments via a mobile game. Learning disabilities are neurological disorders that affect the brain. Children with learning disabilities have trouble with learning compared to their fellow peers and quite often fall back academically since a majority of them go undiagnosed. The specific learning disabilities for which this paper provides screening are dyslexia a reading disability, dyscalculia a mathematical disability, letter dysgraphia and numeric dysgraphia are both writing disabilities. Deep learning and machine learning techniques are used in the screening process of these specific learning disabilities. Trained convolutional neural networks are used to detect the spoken letter/word, detect the written letter/word and detect the written number on the mobile application. Outputs from the convolutional neural network are fed into the models used for screening learning disabilities. The machine learning algorithms used in building the models include k-nearest neighbors, random forest and support vector machine. Screening results from the models built in this research provided an accuracy of 89%, 90%, 92%, 92% for dyslexia, letter dysgraphia, dyscalculia and numeric dysgraphia respectively. This is the first game based screening and intervention tool for dyslexia, letter dysgraphia, dyscalculia and numeric dysgraphia.

A Gamified Approach for Screening and Intervention of Dyslexia, Dysgraphia and Dyscalculia

In recent years, several music recommendation systems have been developed with the aim of incorporating valuable information into the user’s modeling and recommendation process. The inclusion of emotions and contextual information in music recommendation applications is increasingly becoming a relevant aspect to improve the listening experience. Thus, the main aim of this systematic literature review (SLR) is investigating the music recommendation approaches that considers emotions and/or context (research question 1) as well as to identify the main gaps and challenges that still remain and need to be addressed by future research (research question 2). After an extensive research, 64 publications were identified to answer the research questions. The studies were analyzed and evaluated for relevance. The main approaches that consider emotions and context were identified. The results of the review indicate that most studies in the field that combine multiple approach related to emotions or context factors have improved the user’s hearing experience. The main contributions of this review are a set of aspects that we consider important to be addressed by the music recommendation systems, such as: user activity, satisfaction, feedback, cold-start problems, cognitive load, learning, personality, and user preference. In addition, we also present a broad discussion about the challenges, difficulties and limitations that exist in music recommendation systems that consider emotions and contextual factors. 

/pdf/considering-emotions-and-contextual-factors-in-music-2z70zy6a.pdf

Considering emotions and contextual factors in music recommendation: a systematic literature review

Due to growing social network traffic through Internet service provider, businesses are creating applications or systems that tap on social data to study user behavior and in turn, enhance the user experience. This paper proposes an airline opinion mining system (AOMS) that mines and analyzes data from social media such as Twitter, Instagram and Facebook. AOMS is developed using Opinion Mining methodology with four modules, which are Problem Areas, Team Performance, High Priority, and Online Feedback. AOMS was evaluated via Unit Testing to resolve all logical and system errors. The prototype of AOMS system is to be further tested via User Acceptance Test (UAT) by studying user experience in a pilot airline industry.

Opinion Mining in the Airline Industry: Learning Social Sentiments and Insights

The impact of the Internet on education has been recognized for decades in which learners or students are able to access educational resources cross borders and languages. However, exposure to or provision of too much information or data will lead to information overload, which dampens understanding and effective decision making. In this paper, we share a software named Otowik that is able to extract keywords and keyphrases from wiki pages and automatically construct a mind map based on the keywords and keyphrases extracted. Otowik was evaluated via a User Acceptance Test and was highly recommended by the participating respondents.

Mohamad Firdaus Che Abdul Rani

Papers

Opinion Mining in the Airline Industry: Learning Social Sentiments and Insights

Otowik: Mining Text from Wikis to Mind Maps